{"id":"W4311138016","doi":"10.1016/j.patter.2022.100655","title":"Early prediction and longitudinal modeling of preeclampsia from multiomics","year":2022,"lang":"en","type":"article","venue":"Patterns","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Stanford Maternal and Child Health Research Institute; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of General Medical Sciences; School of Medicine, Stanford University; Chan Zuckerberg Initiative; Bill and Melinda Gates Foundation; Stanford University; National Institutes of Health; March of Dimes Foundation; Burroughs Wellcome Fund; Foundation for the National Institutes of Health","keywords":"Preeclampsia; Pregnancy; Receiver operating characteristic; Cohort; Confidence interval; Medicine; Population; Univariate analysis; Area under the curve; Univariate; Cohort study; Internal medicine; Bioinformatics; Obstetrics; Biology; Multivariate analysis; Computer science; Machine learning; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002054612,0.0004944972,0.0004632381,0.0005199121,0.0001946527,0.0008518642,0.0004370764,0.0003993031,0.0004119297],"category_scores_gemma":[0.003058708,0.0002411741,0.0006371202,0.0003616959,0.0001452323,0.000436838,0.0005392827,0.0005847377,0.0001477139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004421559,"about_ca_system_score_gemma":0.0007081513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005222451,"about_ca_topic_score_gemma":0.004679663,"domain_scores_codex":[0.9996909,0.000144506,0.00001775839,0.00008140304,0.00003091907,0.00003464096],"domain_scores_gemma":[0.9989843,0.0005977413,0.0001775647,0.00008519978,0.00009386135,0.00006141323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009776393,0.0005336289,0.3814645,0.0001040716,0.0008624741,0.0003870503,0.000140164,0.5430204,0.008724481,0.001321936,0.001013682,0.06145002],"study_design_scores_gemma":[0.00001475801,0.0001125852,0.02683181,0.00001029725,0.00006208024,0.00004858702,0.00001763303,0.9705037,0.0008259931,0.001266988,0.0002941304,0.00001141353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9073308,0.0009428131,0.08895622,0.0006956456,0.00003589567,0.00006073877,0.001146158,0.0002595809,0.0005720629],"genre_scores_gemma":[0.9852279,0.0002759916,0.01327193,0.00005267352,0.00001935713,0.00005290116,0.0006851513,0.00001151379,0.00040247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005222451,"threshold_uncertainty_score":0.01086599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745634720873904,"score_gpt":0.2576849203022694,"score_spread":0.2202285730935304,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}